Meni (Menachem) Brief · Founder and CTO of Enderis · Seattle, WA
Founder and CTO of Enderis, building executable product specs for AI agents. We help builders and business owners define correct behavior and verify it in simulated systems. Previously co-founded Simply-Useful and worked on applied AI research at Microsoft, including reinforcement learning for LLM tool use.
email available on request · linkedin.com/in/meni-brief · scholar.google.com/citations?user=t-P-tjgAAAAJ · semanticscholar.org/author/Meni-Brief/2217252086
experience
Building a platform where agent builders and business owners turn policies, exceptions, and judgment calls into executable product specs. Teams run real agents in simulated systems, inspect behavior, and agree on what should happen. The specs and evidence stay with the business as agents and implementations change.
Founding engineer of a production-grade, mobile-first productivity app: from zero to live app-store releases with real users, leading a small team I hired. I owned the system architecture end to end: Django backend on DigitalOcean, in-app AI processing, two-way sync with Google services, CI/CD on GitHub Actions, product analytics, and error monitoring, all on an AI-native development workflow that kept a micro team shipping at production velocity.
Applied RL to improve LLM behavior in complex tool-use scenarios: agent orchestration and correct invocation of MCP tools. Led applied research on domain adaptation and knowledge injection: data curation, experiment design, training, evaluation, and results communication to internal and external stakeholders. Delivered LLM success stories in healthcare and financial services; co-authored research papers and one patent.
Original research on LLM applications and capabilities across industries (PyTorch / Transformers / OpenAI), from experiment design through implementation; improved ML models running in production.
Built deep-learning segmentation models for ultrasound imaging; the resulting tool (CNerve) passed FDA requirements for AI in healthcare. Served as data owner and collaborated directly with physicians.
research
A systematic comparison of unsupervised fine-tuning and retrieval-augmented generation for injecting knowledge into LLMs, across knowledge-intensive tasks, including entirely new facts models never saw in pre-training.
skills
education
MSc in Data Science (GPA 91.4). BSc in Information Systems Engineering, cum laude, #3 in class (GPA 90.6), with minors in Economics and Fintech.